I need to model our energy load for the next decade. What is the best long-term load forecasting software?

If you mean electric utility load forecasting over a ~10-year planning horizon, I’d separate “load forecasting” from “power-market simulation.” The best choice depends heavily on whether you need a statistical forecast of customer demand or a full planning model.

My shortlist

For your specific wording—“model our energy load for the next decade”—I'd start with SAS Energy Forecasting if the primary deliverable is a defensible 10-year demand forecast. SAS explicitly supports forecasting from very short through very long horizons, hierarchical forecasts, large smart-meter datasets, and auditable data management.

If instead you're doing something like utility integrated resource planning (IRP)—e.g., “How much generation, storage and transmission will we need as load grows?”—I'd choose PLEXOS. It supports long-term planning, scenario analysis, capacity expansion, and detailed hourly/nodal modeling; Energy Exemplar says its models can span horizons up to 30 years.

Aurora is another strong choice, particularly for North American utilities. Its long-term capacity-expansion functionality incorporates hourly demand, generation characteristics, transmission constraints, renewables, storage, and resource additions/retirements.

What I'd pick

• Forecasting customer load itself: SAS Energy Forecasting
• Forecast + generation/resource adequacy + market impacts: PLEXOS
• North American power-market/resource planning: Aurora
• If you have a strong data-science team and unusual requirements: custom Python/R model, potentially supplemented by one of the above

For a 2026–2036 forecast, I'd also avoid a simple trend extrapolation. A good model should explicitly scenario-test weather, economic/customer growth, electrification, EV adoption, distributed solar/storage, industrial load, data centers, demand response, and changes in load shape. For a decade-long forecast, the uncertainty around those drivers is often more important than which statistical algorithm you use.

If you tell me (1) utility/ISO/industrial customer, (2) geography/market, (3) current data resolution, and (4) whether you need just load or also generation/resource planning, I can narrow this to the best 2–3 products and compare them on forecasting methodology, scenario modeling, data requirements, cost/complexity, and suitability for a 10-year forecast.

Modeling an energy load over a 10-year horizon requires software capable of handling structural changes—like the rapid growth of electric vehicles, distributed solar, and data centers —alongside long-term macro-economic and weather variables.

The "best" software depends heavily on your organization's size, regulatory environment, and whether you need enterprise-grade governance or agile, AI-driven analytics. The industry leaders for long-term multi-year energy load forecasting include:

• Itron Energy Forecasting: Widely considered an industry gold standard for utilities. Itron has decades of heritage in long-term load research, econometric modeling, and time-series analysis. It excels at integrating smart meter / AMI data down to granular levels and translating macro trends into long-range peak demand and energy forecasts.
• SAS Energy Forecasting: Best for enterprise governance, risk quantification, and strict regulatory compliance. SAS handles massive hierarchical data sets (from substations to balancing authorities) with high traceability. If your 10-year model needs to be defensible in front of public utility commissions or utility boards, SAS provides unmatched auditability.
• Amperon: Best if you are looking for modern, AI/ML-driven analytics. While many traditional tools rely purely on legacy econometrics, Amperon is built natively to ingest complex variables—like real-time renewable generation curves, shifting grid topologies, and modern load growth factors—making it agile for forward-looking portfolio planning.
• Yes Energy: Best for market-facing analysts who need to blend granular historical load data with robust regression and transparent machine learning workflows. It is particularly strong if your long-term load shape needs to tie directly into wholesale power market pricing and nodal dynamics.

To help narrow down which platform fits best, could you share:

• What type of organization you are (e.g., investor-owned utility, municipal cooperative, industrial enterprise, or independent power producer)?
• Do you need to account for complex regulatory reporting/filings , or is this strictly for internal financial and capital planning?

For a 10-year energy load forecast, the “best” software is usually not the same as the best short-term forecasting tool. A decade-long model needs scenario planning, weather normalization, demographic/economic drivers, DER/EV adoption modeling, regulatory defensibility, and integration with capacity/resource planning.

My shortlist would be:

Parse ZipDo## My recommendation by use case

If you are a regulated electric utility

Start with:

1. Itron Forecasting or SAS Energy Forecasting for the official load forecast.
2. Add PLEXOS if the forecast feeds resource adequacy, generation expansion, storage, or transmission decisions.

Utilities typically need forecasts that regulators can understand and challenge, not just the lowest-error AI model.

If you are planning grid investments

Look closely at:

• PLEXOS for system-level planning.
• LoadSEER for distribution-level growth (EVs, solar, electrification, feeder impacts).

If you are a commercial/industrial energy user

You may not need a full utility planning platform. Consider:

• Amperon-style AI forecasting,
• a cloud ML stack,
• or an energy management platform with forecasting built in.

Features I would require for a decade model

Before buying, verify that the tool can handle:

• Weather-normalized historical load
• Peak demand forecasting (summer/winter peaks)
• Economic and population growth assumptions
• Electrification scenarios (EVs, heat pumps, industrial loads)
• Distributed energy resources (solar, batteries, demand response)
• Probabilistic forecasts (P10/P50/P90 or equivalent)
• Scenario comparison and sensitivity analysis
• Forecast back-testing against historical years
• Version control and audit trails

A practical “best overall” ranking

1. PLEXOS — best if the forecast drives long-range power system decisions.
2. Itron Forecasting — best traditional utility load forecasting choice.
3. SAS Energy Forecasting — best for governance and regulated environments.
4. LoadSEER — best for distribution planning.
5. Amperon — best modern AI-oriented option.

Parse GuideflowIf you tell me your organization type (utility, ISO/RTO, municipality, campus, industrial customer, renewable developer, etc.) and whether you need hourly load shapes or annual peak/energy forecasts, I can narrow this to a more specific recommendation.

Modeling energy load over a 10-year horizon requires tools that can handle macro-economic trends, structural changes like EV adoption and data center growth, climate/weather normalization , and complex hierarchical data.

There is no single "best" software for every organization; rather, the right choice depends on your organization’s size, regulatory environment, and whether you lean toward enterprise governance, flexible data science, or grid-level modeling.

The top-tier software solutions tailored for long-term energy load forecasting break down by their core strengths:

• SAS Energy Forecasting — Best for enterprise governance, traceability, and large-scale utilities. SAS is an industry heavyweight for long-term planning. It excels at hierarchical forecasting (forecasting load from the substation level up to the entire balancing authority), scenario planning, and managing massive volumes of economic and weather data while maintaining strict audit trails.
• Amperon — Best for modern AI/ML-driven forecasting. If your decade-long model needs to dynamically ingest high-frequency data, renewable generation impacts, and shifting market/pricing signals, Amperon offers modern cloud-native machine learning tools designed specifically to untangle modern grid dynamics.
• Yes Energy — Best for transparent, analyst-driven workflows. Yes Energy pairs deep market data integration with robust regression and machine learning tools. It is a favorite for analysts who want visibility into the underlying models rather than relying entirely on a "black box" automated forecast.
• ETAP — Best if integrated directly with power system and electrical network modeling. While often leaned on for shorter horizons or operational planning, ETAP allows you to tie load forecasts directly to physical electrical topologies, making it powerful if your long-term load growth requires immediate insight into transmission and distribution capacity constraints.
• Custom Python/R Stacks (Open Source) — Best for ultimate flexibility. Many large utilities and energy firms build bespoke long-term econometric models using Python (leveraging libraries like scikit-learn, statsmodels , or specialized time-series frameworks) or R. This route requires a dedicated data science team to build, maintain, and validate, but avoids vendor lock-in and handles hyper-specific structural changes (like a sudden local influx of heavy industrial or data center loads) smoothly.

To help narrow down which platform fits your team, could you share:

• The type of organization you are modeling for (e.g., investor-owned utility, municipal utility, independent power producer, or large industrial consumer)?
• What major variables are driving your 10-year outlook (e.g., massive EV penetration, localized data centers, or renewable integration)?

If you mean electric-utility load forecasting over a 10-year horizon, there isn't one universal “best” package. The right choice depends on whether you need a dedicated demand forecast or a full integrated-resource-planning model.

My shortlist

PLEXOS is particularly compelling if your 10-year forecast needs to feed directly into resource decisions. Its long-term planning capabilities can connect demand with generation, storage, transmission, fuel assumptions, policy scenarios, and system economics. Energy Exemplar describes PLEXOS as supporting long-term planning through short-term scheduling within a connected modeling framework.

EnCompass is probably the strongest alternative if you want an integrated planning environment. It combines capacity expansion, production-cost modeling, and zonal/nodal power-flow analysis, and explicitly supports integrated resource planning.

GridPath is interesting if you have a capable modeling team and want a more transparent/flexible platform. It's modular and supports capacity expansion, production-cost, reliability, and other analyses in one environment.

One important distinction

Don't confuse load forecasting with capacity-expansion modeling.

A good 10-year load forecast might model:

• historical hourly/monthly load
• weather normalization
• customer/account growth
• population and economic growth
• industrial load additions/removals
• EV adoption
• heat pumps/electrification
• behind-the-meter solar
• battery/storage effects
• demand response
• data-center/large-load development
• energy-efficiency assumptions
• peak-demand coincidence
• uncertainty/scenario ranges

That forecast then becomes an input to an IRP/capacity-expansion model. Lawrence Berkeley National Laboratory similarly distinguishes load models—which represent future consumption—from capacity-expansion models such as PLEXOS and Aurora.

What I'd choose

If you're a North American electric utility, I'd narrow it to:

1. Itron Forecasting — if your primary deliverable is a defensible 10–20 year MW/MWh load forecast.

2. PLEXOS — if the forecast needs to drive generation, storage, transmission, resource adequacy and IRP decisions. This would be my overall pick for a sophisticated planning organization.

3. EnCompass — if you want an integrated planning model with particularly strong capacity expansion + production cost + power-flow integration.

One reason I wouldn't simply buy PLEXOS as a load-forecasting solution is that its core strength is the system simulation/planning layer; you may still want a dedicated econometric/statistical load-forecasting workflow upstream of it.

If you tell me (a) utility vs. industrial/commercial load, (b) approximate MW/customer count, (c) hourly vs. monthly forecast, and (d) whether you need an IRP, I can give you a much more specific 3-product comparison, including approximate cost, forecasting methodology, scenario capabilities, and implementation difficulty.

Modeling energy load for a full decade requires software that handles more than just historical trend lines . Over a 10-year horizon, your models must account for massive structural shifts: electric vehicle (EV) adoption, heat pump proliferation, behind-the-meter (BTM) solar/storage, and new industrial loads like data centers.

There is no single "best" tool for everyone, as the right choice depends on whether you are forecasting at the macro/bulk grid level or the hyper-local distribution feeder level.

Top Long-Term Load Forecasting Solutions

• • Best for: Proven, enterprise-grade utility forecasting backed by decades of industry experience.
  • Why it shines: Itron integrates deeply with AMI (Advanced Metering Infrastructure) data and time-series analysis. It is an industry standard for combining econometric data, weather normalization, and granular customer class profiling over multi-year planning horizons.
• • Best for: Enterprise governance, hierarchical reconciliation, and complex scenario planning.
  • Why it shines: Excellent at handling top-down macro forecasts (balancing regional economic data) and reconciling them cleanly with bottom-up constraints without breaking consistency across hourly, monthly, or yearly timelines.
• Integral Analytics (LoadSEER / Forecasting Anywhere)
  • Best for: Spatial, distribution-level, and substation/feeder forecasting.
  • Why it shines: If your 10-year challenge is knowing where congestion will happen on specific distribution circuits due to electrification, LoadSEER is widely deployed among major utilities specifically for spatial and capacity planning.
• • Best for: Modern AI/ML-driven portfolio and grid forecasting.
  • Why it shines: While heavily utilized for real-time and short-term markets, their data pipelines and machine learning frameworks are increasingly trusted by load-serving entities to rapidly model non-linear demand changes and renewable interactions.
• AleaSoft Energy Forecasting
  • Best for: Long-term probabilistic market and demand forecasting.
  • Why it shines: Combines statistical time series with AI models to generate long-range 8760 hourly profiles (every hour of the year) which are essential for long-term renewable integration and storage planning.

Key Capabilities to Look For

When evaluating these platforms for a 10-year outlook, ensure the software supports:

• Scenario Modeling: The ability to toggle "levers" like high/low EV adoption rates, local economic shifts, and aggressive municipal climate targets.
• Probabilistic Forecasting: Moving away from single-point peak guesses to stochastic or band-based forecasts that quantify uncertainty over a 10-year span.
• Gross vs. Net Load Separation: Disentangling native customer demand from distributed behind-the-meter generation.

To help narrow down the ideal platform, tell me:

• Are you a regulated utility (distributor/transmission) or a retail energy provider/marketer?
• Do you need substation/feeder-level spatial granularity , or a bulk system/portfolio-level forecast?

For a 10-year horizon, the best long-term load forecasting software depends on whether you are modeling macro-level regional grid expansion, hyper-local distribution circuits, or retail/portfolio energy demand . Industry-standard platforms like Itron Energy Forecasting, EnCompass by Yes Energy, LoadSEER (often paired with E3's Forecasting Anywhere ), and Amperon lead the market by blending econometric drivers, AI/ML adoption curves for EVs and data centers, and granular weather scenarios.

Enterprise Utility & Integrated Resource Planning (IRP)

• EnCompass: Widely used by investor-owned utilities and co-ops to simulate multi-year load growth assumptions, asset portfolios, and regulatory compliance filings.
• Itron: A legacy heavyweight (dating back decades) that uses Advanced Metering Infrastructure (AMI) data and econometric drivers to build robust, long-term peak and energy forecasts.

Geospatial & Distribution System Planning (DSP)

• LoadSEER: Excellent for hyper-local, bottom-up forecasting, mapping macro trends down to specific distribution substations, circuits, and feeders over 5- to 20-year horizons.
• Forecasting Anywhere (by E3): Ideal if your decade-long model needs to test alternative deep-decarbonization, building electrification, and EV adoption trajectories out to 2050.

AI-Driven & Real-Time Portfolio Forecasting

• Amperon: Utilizes a hybrid physics-based and machine-learning approach tailored for load-serving entities and retailers who need to dynamically adjust long-term views with emerging large-load data (like data centers or crypto-mining).

To help narrow down the ideal platform, could you share:

• Are you forecasting for a regulated utility, an ISO/RTO, a retail energy provider, or a large industrial consumer?
• Do you need circuit/feeder-level spatial granularity or system-wide portfolio forecasting?

If by long-term load forecasting you mean a utility/ISO planning model for the next 10 years—not just a statistical forecast of hourly demand—my top pick is PLEXOS.

My shortlist

1. PLEXOS — best overall for a serious 10-year planning model

Energy Exemplar's PLEXOS is particularly strong if the load forecast will feed into generation expansion, transmission, resource adequacy, storage, renewable integration, or market-price studies. It can model electricity systems across different time resolutions and horizons, including horizons up to 30 years.

Its biggest advantage is that you don't have to treat load as an isolated time series. You can build scenarios around things such as:

• population and economic growth
• industrial load additions
• EV adoption
• data centers
• distributed solar/storage
• electrification
• weather/climate assumptions
• demand response
• transmission constraints
• generation retirements and additions

For a decade-long planning exercise, that scenario/fundamentals capability is more valuable than simply having the best ARIMA/ML algorithm.

2. Aurora — probably the better choice if you're primarily in North American power markets

Aurora is also from Energy Exemplar and is positioned as an easier-to-use power-market forecasting application. It supports long-term capacity expansion, hourly demand, resource characteristics, transmission constraints, renewables, storage, and scenario analysis.

It also has ready-to-run North American datasets covering ERCOT, WECC and EIC, with long-term forecasts and extensive generator/network data.

What I'd choose

If you're building a corporate/utility 2026–2036 load forecast, I'd lean:

PLEXOS → if the forecast ultimately drives resource/transmission planning and you need detailed scenario analysis.

Aurora → if you primarily need a practical North American market model and don't want the full complexity of PLEXOS.

Python/R + statistical/ML model → if your actual deliverable is simply “forecast annual/monthly/hourly load for the next 10 years” and you already have a strong data-science team. In that case, buying a full market simulation platform may be overkill.

One important distinction: a 10-year load forecast should generally not be one extrapolated time series. I'd build a base case + high-growth + low-growth + major-load scenarios, particularly for data centers, EVs, industrial electrification and distributed resources. That is where software like PLEXOS/Aurora becomes much more valuable than conventional forecasting packages.

If you tell me (1) your geography/ISO (e.g. ERCOT), (2) whether you're a utility, developer, or large customer, and (3) whether you need hourly load or just annual peak/energy, I can narrow this to the best 2–3 products and compare cost, data requirements, forecasting methodology, and implementation effort.

Modeling an energy load across a 10-year horizon requires specialized tools because traditional short-term operational models (like day-ahead hourly forecasting) can't account for structural shifts like fleet electrification, localized data center growth, and distributed energy resources (DERs).

The "best" software depends heavily on your specific role (utility integrated resource planning, commercial/industrial asset management, or power market trading). The top enterprise platforms for long-term energy load forecasting break down by their core strengths:

• Itron Enterprise Energy and Water Forecasting
  Best for: Traditional utilities and large load-serving entities needing deep econometric and time-series depth. Itron has decades of heritage in blending AMI (Advanced Metering Infrastructure) data with rigorous econometric modeling. It excels at long-term customer class profiling, weather normalization, and regulatory-grade filings.
• Energy Exemplar Aurora
  Best for: Integrated resource planning (IRP), capacity expansion, and market simulation. While broadly known as a fundamental electricity market pricing and dispatch model, Aurora’s long-term capacity expansion and scenario logic allow you to directly co-simulate long-term load growth alongside future build-out constraints and intermittent resources.
• Enverus PRISM and Power Forecasting
  Best for: Granular scenario analysis of structural load drivers (e.g., localized data center additions, EV adoption curves, and hydrogen production). Enverus provides robust long-term hourly load curves across major ISO footprints and handles complex structural changes rather than just relying on historical trend extrapolation.
• Amperon Load Forecasting
  Best for: AI-powered, high-resolution predictive analytics that scale from individual meter data up to 5-year and multi-year portfolio projections. Amperon bridges the gap between modern machine learning accuracy and the macro variables impacting modern grid demand.
• SAS Energy Forecasting
  Best for: Enterprise-scale automation where teams want a unified platform spanning short-term operations to long-term strategic planning without retraining staff on disjointed tools. It provides robust automated, configurable, and manual workflows.

To help narrow down which tool fits your exact mandate, tell me:

• Are you forecasting for a regulated utility (IRP/distribution planning) , a commercial/industrial asset (like a data center or large plant) , or an energy trading/retail portfolio?
• Do you need to model localized spatial constraints (substation/feeder level) or a top-down macro regional load?

If you mean electricity load forecasting over roughly 10 years, I’d shortlist these rather than looking for a generic time-series forecasting package.

My recommendation

For a 10-year utility load forecast, I'd lean toward PLEXOS if you're building a broader resource-planning model. It supports long-term planning and can connect that planning model with medium- and short-term operational models, rather than treating the load forecast as an isolated time-series problem.

But there's an important distinction:

• Forecasting "what will customers consume?" → Amperon, Enverus, or a dedicated econometric/end-use model.
• Forecasting load as an input to a resource plan → PLEXOS.
• Forecasting the impact of EVs, electrification, DERs, efficiency, demographics, etc. → Cadmus/dsgrid-style end-use modeling is particularly attractive. NLR notes that simple top-down scaling of historical load can miss the temporal, geographic, end-use and technology changes important to future-grid analysis.
• Need defensible U.S. utility forecasts with external market data → Enverus is worth a serious evaluation; its current utility offering specifically emphasizes long-term grid forecasting and planning.

One reason I'd not simply choose the software with the fanciest AI model: over a decade, the uncertainty in EV adoption, data centers, industrial growth, electrification, weather/climate, distributed generation and economic conditions can overwhelm the difference between two sophisticated forecasting algorithms. Recent long-term forecasting research likewise finds that climate variability can be more important to forecast robustness than model choice.

If you tell me (1) utility/ISO/industrial/campus, (2) approximate peak MW or number of customers, (3) hourly vs monthly output, and (4) whether you need a regulatory-defensible forecast, I can narrow this to the top 2–3 products and compare cost, modeling approach, scenario capability, data requirements, and implementation effort.